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In this paper, we present an efficient method for visual descriptors retrieval based on compact hash codes computed using a multiple k-means assignment. The method has been applied to the problem of approximate nearest neighbor (ANN) search of local and global visual content descriptors, and it has been tested on different datasets: three large scale standard datasets of engineered features of up...
In this paper we present an efficient and effective method for visual descriptors hashing based on hierarchical multiple assignment within a k-means framework. The method has been used to address the problem of approximate nearest neighbor (ANN) retrieval, and it has been tested on local and global visual content descriptors, either engineered or learned. The proposed method has been compared to state-of-the-art...
This paper presents a novel method for efficient image retrieval, based on a simple and effective hashing of CNN features and the use of an indexing structure based on Bloom filters. These filters are used as gatekeepers for the database of image features, allowing to avoid to perform a query if the query features are not stored in the database and speeding up the query process, without affecting...
In this paper we present an efficient method for mobile visual search that exploits compact hash codes and data structures for visual features retrieval. The method has been tested on a large scale standard dataset of one million SIFT features, showing a retrieval performance comparable or superior to state-of-the-art methods, and a very high efficiency in terms of memory consumption and computational...
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